import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

df = pd.read_excel('./hty/venv/association_rules/餐厅数据.xlsx')
print(df.head)

trsations = df['菜品'].str.split(',').to_list()

te = TransactionEncoder()
te_ary = te.fit(trsations).transform(trsations)
df_enecoded = pd.DataFrame(te_ary, columns=te.columns_)

frequent_itemsets = apriori(df_enecoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)

print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x) ==2)])

rules= association_rules(frequent_itemsets,metric="confidence",min_threshold=0.1)

effective =rules[
    (rules['lift'] >1)&(rules['confidence'] > 0.1)
].sort_values(by=['lift','confidence'],ascending=False)

print(effective[['antecedents','consequents','support','confidence','lift']])

import matplotlib.pyplot as plt
import networkx as nx

plt.rcParams['font.sans-serif'] =['SimHei']
plt.rcParams['axes.unicode_minus'] = False

G=nx.DiGraph()

for _, row in effective.iterrows(): 
   G.add_edge(','.join(list(row['antecedents'])),
              ','.join(list(row['consequents'])),
               weight=row['lift'])

plt.figure(figsize=(12, 8))
pos = nx.spring_layout(G)
                

nx.draw(G,pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u,v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues
)

plt.show()

